| import gym |
| import numpy as np |
| import pytest |
|
|
| from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3 |
| from stable_baselines3.common.evaluation import evaluate_policy |
|
|
|
|
| class DummyMultiDiscreteSpace(gym.Env): |
| def __init__(self, nvec): |
| super(DummyMultiDiscreteSpace, self).__init__() |
| self.observation_space = gym.spaces.MultiDiscrete(nvec) |
| self.action_space = gym.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32) |
|
|
| def reset(self): |
| return self.observation_space.sample() |
|
|
| def step(self, action): |
| return self.observation_space.sample(), 0.0, False, {} |
|
|
|
|
| class DummyMultiBinary(gym.Env): |
| def __init__(self, n): |
| super(DummyMultiBinary, self).__init__() |
| self.observation_space = gym.spaces.MultiBinary(n) |
| self.action_space = gym.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32) |
|
|
| def reset(self): |
| return self.observation_space.sample() |
|
|
| def step(self, action): |
| return self.observation_space.sample(), 0.0, False, {} |
|
|
|
|
| @pytest.mark.parametrize("model_class", [SAC, TD3, DQN]) |
| @pytest.mark.parametrize("env", [DummyMultiDiscreteSpace([4, 3]), DummyMultiBinary(8)]) |
| def test_identity_spaces(model_class, env): |
| """ |
| Additional tests for DQ/SAC/TD3 to check observation space support |
| for MultiDiscrete and MultiBinary. |
| """ |
| |
| if model_class == DQN: |
| env.action_space = gym.spaces.Discrete(4) |
|
|
| env = gym.wrappers.TimeLimit(env, max_episode_steps=100) |
|
|
| model = model_class("MlpPolicy", env, gamma=0.5, seed=1, policy_kwargs=dict(net_arch=[64])) |
| model.learn(total_timesteps=500) |
|
|
| evaluate_policy(model, env, n_eval_episodes=5, warn=False) |
|
|
|
|
| @pytest.mark.parametrize("model_class", [A2C, DDPG, DQN, PPO, SAC, TD3]) |
| @pytest.mark.parametrize("env", ["Pendulum-v0", "CartPole-v1"]) |
| def test_action_spaces(model_class, env): |
| if model_class in [SAC, DDPG, TD3]: |
| supported_action_space = env == "Pendulum-v0" |
| elif model_class == DQN: |
| supported_action_space = env == "CartPole-v1" |
| elif model_class in [A2C, PPO]: |
| supported_action_space = True |
|
|
| if supported_action_space: |
| model_class("MlpPolicy", env) |
| else: |
| with pytest.raises(AssertionError): |
| model_class("MlpPolicy", env) |
|
|
